Dataset Description:

	This dataset contains annotated images of maize kernels for object detection and health classification tasks. 
	Each image may contain a single or multiple maize kernels with varying health conditions.

Image Data:

	Image format: JPG

	Naming convention:

	Image_naming_convention:IMG_YYYYMMDD_HHMMSS.jpg

	Label_naming_convention:IMG_YYYYMMDD_HHMMSS.txt

Annotation Format:

	Annotations are provided as text files (.txt) using the YOLO bounding box format.

	Each line in a label file corresponds to one maize kernel and follows the format:

	<class_id> <x_center> <y_center> <width> <height>

Example:
	1 0.518383 0.537347 0.346507 0.254520

All bounding box coordinates are normalized to the image dimensions and range between 0 and 1.

Class Definitions:

	0 = Healthy maize kernel
	1 = Unhealthy maize kernel

Class labels are assigned per kernel, and a single image may contain multiple kernels of different health conditions.

Dataset Statistics:

	Total images: 5,143
	Total objects (kernels): 13,533

	Healthy: 7,337
	Unhealthy: 6,196

Image–Label Alignment:

	Every image has a corresponding label file.
	Every label file has a corresponding image.
	There is a one-to-one correspondence between image filenames and label filenames.

Intended Use of the dataset:

	This dataset is intended for research and educational purposes, particularly in object detection, computer vision, and agricultural grain health analysis.


Important Note on Uploads:
- The 'images' folder is large and has been split into multiple archives using 7-Zip (e.g., images.zip.001, images.zip.002, …) due to the 2.5 GB per file upload       
  limit on Harvard Dataverse. Users must download all parts and recombine them.

- The 'labels' folder is small (few KBs) and has been provided as a **single archive (labels.zip)**. No splitting is required.


Recommended Steps:
1. Download all parts (e.g., images.7z.001, images.7z.002, ...images.7z.016).
2. Open `images.7z.001` with 7-Zip. This will extract all files to reconstruct the original folder structure.
3. Repeat for the labels.zip parts.

Tools:
- 7-Zip (Free and Open Source): https://www.7-zip.org/
- WinRAR or other compatible tools can also be used.







